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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: summarise_v9
results: []
---
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3650
- Rouge1 Precision: 0.4673
- Rouge1 Recall: 0.4135
- Rouge1 Fmeasure: 0.4263
- Rouge2 Precision: 0.1579
- Rouge2 Recall: 0.1426
- Rouge2 Fmeasure: 0.1458
- Rougel Precision: 0.2245
- Rougel Recall: 0.2008
- Rougel Fmeasure: 0.2061
- Rougelsum Precision: 0.2245
- Rougelsum Recall: 0.2008
- Rougelsum Fmeasure: 0.2061
## Model description
This model was created to generate summaries of news articles.
## Intended uses & limitations
The model takes up to maximum article length of 3072 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|
| 2.8095 | 0.16 | 10 | 2.5393 | 0.287 | 0.5358 | 0.3674 | 0.1023 | 0.1917 | 0.1311 | 0.1374 | 0.2615 | 0.1771 | 0.1374 | 0.2615 | 0.1771 |
| 2.6056 | 0.32 | 20 | 2.4752 | 0.5005 | 0.3264 | 0.3811 | 0.1663 | 0.1054 | 0.1249 | 0.2582 | 0.1667 | 0.1957 | 0.2582 | 0.1667 | 0.1957 |
| 2.5943 | 0.48 | 30 | 2.4422 | 0.4615 | 0.3833 | 0.4047 | 0.1473 | 0.1273 | 0.1321 | 0.2242 | 0.1885 | 0.1981 | 0.2242 | 0.1885 | 0.1981 |
| 2.4842 | 0.64 | 40 | 2.4186 | 0.4675 | 0.3829 | 0.4081 | 0.1581 | 0.1294 | 0.1384 | 0.2286 | 0.187 | 0.1995 | 0.2286 | 0.187 | 0.1995 |
| 2.4454 | 0.8 | 50 | 2.3990 | 0.467 | 0.408 | 0.4222 | 0.1633 | 0.1429 | 0.1477 | 0.2294 | 0.2008 | 0.2076 | 0.2294 | 0.2008 | 0.2076 |
| 2.3622 | 0.96 | 60 | 2.3857 | 0.4567 | 0.3898 | 0.41 | 0.1433 | 0.1233 | 0.1295 | 0.2205 | 0.1876 | 0.1976 | 0.2205 | 0.1876 | 0.1976 |
| 2.4034 | 1.13 | 70 | 2.3835 | 0.4515 | 0.4304 | 0.4294 | 0.1526 | 0.1479 | 0.1459 | 0.2183 | 0.209 | 0.2078 | 0.2183 | 0.209 | 0.2078 |
| 2.2612 | 1.29 | 80 | 2.3804 | 0.455 | 0.4193 | 0.4236 | 0.1518 | 0.1429 | 0.1427 | 0.2177 | 0.2025 | 0.2037 | 0.2177 | 0.2025 | 0.2037 |
| 2.2563 | 1.45 | 90 | 2.3768 | 0.4821 | 0.391 | 0.4196 | 0.1652 | 0.1357 | 0.144 | 0.2385 | 0.1929 | 0.2069 | 0.2385 | 0.1929 | 0.2069 |
| 2.243 | 1.61 | 100 | 2.3768 | 0.4546 | 0.4093 | 0.4161 | 0.1552 | 0.1402 | 0.1422 | 0.2248 | 0.2016 | 0.2052 | 0.2248 | 0.2016 | 0.2052 |
| 2.2505 | 1.77 | 110 | 2.3670 | 0.4625 | 0.4189 | 0.4262 | 0.1606 | 0.1485 | 0.1493 | 0.2301 | 0.2098 | 0.2119 | 0.2301 | 0.2098 | 0.2119 |
| 2.2453 | 1.93 | 120 | 2.3650 | 0.4673 | 0.4135 | 0.4263 | 0.1579 | 0.1426 | 0.1458 | 0.2245 | 0.2008 | 0.2061 | 0.2245 | 0.2008 | 0.2061 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1
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